Datacenter resource allocation

ABSTRACT

Technologies and implementations for allocating datacenter resources are generally disclosed.

BACKGROUND

Unless otherwise indicated herein, the approaches described in thissection are not prior art to the claims in this application and are notadmitted to be prior art by inclusion in this section.

Current datacenters may perform tasks for clients by allocatingresources at the datacenter to the tasks. With conventional datacenters,the resources may be inefficiently allocated and, in some instances, thecolocation of certain tasks to shared hardware may stress and/orinefficiently utilize the shared hardware.

SUMMARY

The present disclosure describes examples methods for allocatingdatacenter resources and/or generating a task fingerprint and resourceallocation database. Example methods for allocating resources at adatacenter may include receiving a task at the datacenter, generating atask fingerprint based on the received task, comparing the taskfingerprint to a plurality of reference task fingerprints to determine aclosest match reference task fingerprint, determining a reference taskbehavior profile related to the closest match reference taskfingerprint, and allocating resources to perform the received task basedon the reference task behavior profile.

Example methods for generating a task fingerprint and resourceallocation database may include receiving a plurality of tasks at adatacenter, generating a task fingerprint for each received task to forma plurality of task fingerprints, allocating a resource set to eachreceived task to define a plurality of allocated resource sets,performing each received task using the respective allocated resourceset, gathering a task behavior for each performed task using theallocated resource set to generate a plurality of task behaviors, andrelating, in a database at the datacenter, each task fingerprint,allocated resource set, and task behavior to generate the taskfingerprint and resource allocation database.

The present disclosure also describes example machine readablenon-transitory medium having stored therein instructions that, whenexecuted, cause a datacenter to allocate datacenter resources and/orgenerate a task fingerprint and resource allocation database. Examplemachine readable non-transitory media may have stored thereininstructions that, when executed, cause a datacenter to allocatedatacenter resources by receiving a task at the datacenter, generating atask fingerprint based on the received task, comparing the taskfingerprint to a plurality of reference task fingerprints to determine aclosest match reference task fingerprint, determining a reference taskbehavior profile related to the closest match reference taskfingerprint, and allocating resources to perform the received task basedon the reference task behavior profile.

Example machine readable non-transitory media may have stored thereininstructions that, when executed, cause a datacenter to generate a taskfingerprint and resource allocation database by receiving a plurality oftasks at a datacenter, generating a task fingerprint for each receivedtask to form a plurality of task fingerprints, allocating a resource setto each received task to define a plurality of allocated resource sets,performing each received task using the respective allocated resourceset, gathering a task behavior for each performed task using theallocated resource set to generate a plurality of task behaviors, andrelating, in a database at the datacenter, each task fingerprint,allocated resource set, and task behavior to generate the taskfingerprint and resource allocation database.

The present disclosure also describes example datacenters for allocatingdatacenter resources and/or generating a task fingerprint and resourceallocation database. Example datacenters may include a processor and amachine readable medium having stored therein instructions that, whenexecuted, cause a datacenter to allocate resources by receiving a taskat the datacenter, generating a task fingerprint based on the receivedtask, comparing the task fingerprint to a plurality of reference taskfingerprints to determine a closest match reference task fingerprint,determining a reference task behavior profile related to the closestmatch reference task fingerprint, and allocating resources to performthe received task based on the reference task behavior profile.

Example datacenters may include a processor and a machine readablemedium having stored therein instructions that, when executed, cause adatacenter to generate a task fingerprint and resource allocationdatabase by receiving a plurality of tasks at a datacenter, generating atask fingerprint for each received task to form a plurality of taskfingerprints, allocating a resource set to each received task to definea plurality of allocated resource sets, performing each received taskusing the respective allocated resource set, gathering a task behaviorfor each performed task using the allocated resource set to generate aplurality of task behaviors, and relating, in a database at thedatacenter, each task fingerprint, allocated resource set, and taskbehavior to generate the task fingerprint and resource allocationdatabase.

The foregoing summary may be illustrative only and may not be intendedto be in any way limiting. In addition to the illustrative aspects,embodiments, and features described above, further aspects, embodiments,and features will become apparent by reference to the drawings and thefollowing detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Subject matter is particularly pointed out and distinctly claimed in theconcluding portion of the specification. The foregoing and otherfeatures of the present disclosure will become more fully apparent fromthe following description and appended claims, taken in conjunction withthe accompanying drawings. Understanding that these drawings depict onlyseveral embodiments in accordance with the disclosure and are,therefore, not to be considered limiting of its scope, the disclosurewill be described with additional specificity and detail through use ofthe accompanying drawings.

In the drawings:

FIG. 1 is an illustration of a flow diagram of an example method forallocating resources;

FIG. 2 is an illustration of a flow diagram of an example method forgenerating a task fingerprint and resource allocation database;

FIG. 3 is an illustration of a table of an example data structure forrelated task fingerprint, allocated resource set, and task behaviorinformation;

FIG. 4 is an illustration of a table of an example data structure forrelated task fingerprints and task behavior information;

FIG. 5 is an illustration of a diagram of an example tree flowextraction;

FIG. 6 is an illustration of an example computer program product; and

FIG. 7 is an illustration of a block diagram of an example computingdevice, all arranged in accordance with at least some embodiments of thepresent disclosure.

DETAILED DESCRIPTION

Subject matter is particularly pointed out and distinctly claimed in theconcluding portion of the specification. The foregoing and otherfeatures of the present disclosure will become more fully apparent fromthe following description and appended claims, taken in conjunction withthe accompanying drawings. Understanding that these drawings depict onlyseveral embodiments in accordance with the disclosure and are,therefore, not to be considered limiting of its scope, the disclosurewill be described with additional specificity and detail through use ofthe accompanying drawings.

The following description sets forth various examples along withspecific details to provide a thorough understanding of claimed subjectmatter. It will be understood by those skilled in the art, however, thatclaimed subject matter may be practiced without some or more of thespecific details disclosed herein. Further, in some circumstances,well-known methods, procedures, systems, components and/or circuits havenot been described in detail in order to avoid unnecessarily obscuringclaimed subject matter.

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented here. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe Figures, can be arranged, substituted, combined, and designed in awide variety of different configurations, all of which are explicitlycontemplated and make part of this disclosure.

This disclosure is drawn, inter alia, to methods, devices, systems andcomputer readable media related to allocating datacenter resources.

In some examples, a task may be received at a datacenter. In someexamples, a task fingerprint may be generated for the received task andthe task fingerprint may be used to determine a closest match referencetask fingerprint for the task fingerprint. In various examples, the taskfingerprint and the closest match reference task fingerprint may beperfect matches or imperfect matches (such that the closest match may bea surrogate for the task fingerprint). In some examples, a task behaviorprofile related to the closest match task fingerprint may be used toallocate datacenter resources for performing the received task. In someexamples, the task behavior profile may include preferred hardware toperform the task and conflict tasks (i.e., tasks with which the taskshould not share resources). In some examples, during performance of thetask, the task performance may be monitored and performance metrics maybe gathered such that either the task behavior profile may be updated(if a perfect match existed) or a new task behavior profile entry may becreated for the newly identified task fingerprint. Such methods may berepeated as needed for received tasks and/or to improve a task behaviorprofile database.

In some examples, the discussed techniques may be used to produce a taskfingerprint and resource allocation database. In some examples, thedatabase may be used at the datacenter. In some examples, the databasemay be provided to another datacenter or a newly implemented datacenterto improve the performance at that datacenter. In some examples, thedatabase may be produced in the course of completing tasks for clients.In some examples, the database may be produced in response to a group ofprepared tasks.

FIG. 1 is an illustration of a flow diagram of an example method 100 forallocating resources, arranged in accordance with at least someembodiments of the present disclosure. In general, method 100 may beperformed by any suitable device, devices, or system such as thosediscussed herein. In some examples, method 100 may be performed by adatacenter or datacenter cluster, or the like. In some examples, method100 may be performed by a cloud computing service. In some examples,method 100 may be performed by an Infrastructure as a Service (laaS)provider or a Platform as a Service (PaaS) provider, or the like. Insome examples, method 100 may be performed by system as discussed hereinwith respect to FIG. 7.

Method 100 sets forth various functional blocks or actions that may bedescribed as processing steps, functional operations, events and/oracts, etc., which may be performed by hardware, software, and/orfirmware. Numerous alternatives to the functional blocks shown in FIG. 1may be practiced in various implementations. For example, interveningactions not shown in FIG. 1 and/or additional actions not shown in FIG.1 may be employed and/or some of the actions shown in FIG. 1 may beeliminated, without departing from the scope of claimed subject matter.Method 100 may include one or more of functional operations as indicatedby one or more of blocks 110, 120, 130, 140, 150, 160, 170 and/or 180.The process of method 100 may begin at block 110.

At block 110, “Receive a Task”, a task may be received at a datacenter,for example. In general, the task may be received using any suitablecommunications technique or techniques. In general, the received taskmay include any suitable task, group of tasks, or subtask, or the like.In some examples, the task may be an application. In some examples, thetask may be a process. In some examples, the task may be a standardand/or library task provided by the datacenter. In some examples, thetask may be a client written task. In general, the task may be receivedfrom any suitable client, client system, or user system, or the like.The process of method 100 may continue at block 120.

At block 120, “Generate a Task Fingerprint Based on the Received Task”,a task fingerprint may be generated, at the datacenter, based at leastin part on the received task. In general, the task fingerprint may begenerated using any suitable technique or techniques that may enablerecognition and/or matching of recurring tasks received at thedatacenter. In general, a task fingerprint may include a compactrepresentation of the task. In some examples, the task fingerprint maybe generated by identifying and/or mapping subtasks within the task. Insome examples, the task fingerprint may be generated by performing ahash function on the received task. In some examples, the taskfingerprint may be generated by sub-fingerprint masking some of amultiple number of subtasks within the task and identifying unmaskedsubtasks of the task. In such implementations, algorithms may be used asanalogous to those used in audio fingerprinting applications, forexample. In some examples, the task fingerprint may be generated byperforming a tree flow extraction on the received task and providing asymbolic approximation representation of the tree flow extraction, as isdiscussed further herein and, in particular, with respect to FIG. 5below.

In general, the task fingerprint may be performed by any suitableresource of a datacenter as discussed herein such as, for example, acomputer, a multiple number of computers, a server, a computingresource, a virtual machine, or a computing cluster, or the like. Insome examples, the task fingerprint may be performed by a dedicatedcomputing resource. In some examples, the task fingerprint may beperformed by a task fingerprint generator at the datacenter. In someexamples, generating the task fingerprint may include accessing thereceived task using a hypervisor level access, a board level access, aprocessor core level access, or a virtual machine level access, or thelike.

As discussed, in some examples, a task fingerprint may be generated at adatacenter. In some examples, a task fingerprint may be received from aclient such as, for example, a client system, or user system, or thelike. In such examples, the processing required to generate a taskfingerprint may thereby be offloaded from the data center to the clientside. In general, the task fingerprint may be formed at the client sideusing any of the techniques discussed herein and the task fingerprintmay be transmitted from the client side using any suitablecommunications techniques. The process of method 100 may continue atblock 130.

At block 130, “Compare the Task Fingerprint to Reference TaskFingerprints to Determine a Closest Match Reference Task Fingerprint”,the generated task fingerprint may be compared to one or more referencetask fingerprints to determine a closest match reference taskfingerprint. In general, the comparison may be made using any suitabletechnique or techniques. In some examples, the comparison may includecomparing the results of hash functions related to the task fingerprintand the reference task fingerprints. In some examples, the comparisonmay include comparing the results of sub-fingerprint masking related tothe task fingerprint and the reference task fingerprints. In someexamples, the comparison may include comparing the results of tree flowextractions and symbolic representations related to the task fingerprintand the reference task fingerprints. In some examples, each comparisonbetween the task fingerprint and a reference task fingerprint maygenerate a similarity score and the highest similarity score may providethe closest match. In general, the task fingerprint may be performed byany suitable resource of a datacenter as discussed herein. In someexamples, the fingerprint comparison may be performed by a dedicatedcomputing resource. In some examples, the fingerprint comparison may beperformed by a task manager at the datacenter. The process of method 100may continue at block 140.

At block 140, “Determine a Reference Task Behavior Profile Related tothe Closest Match Reference Task Fingerprint”, a reference task behaviorprofile related to the closest match reference task fingerprint may bedetermined. In general, the reference task behavior profile may bedetermined using any suitable technique or techniques. In some examples,the comparison discussed at block 130 may generate a score or rankingfor each comparison such that a highest score or ranking may indicate aclosest match reference task fingerprint. In some examples, the taskfingerprint and the closest match reference task fingerprint may be anexact or perfect match such that the match may indicate the task hasbeen performed previously at the datacenter. In some examples, the taskfingerprint and the closest match reference task fingerprint may not bean exact or perfect match. In such examples, the closest match referencetask fingerprint may be considered a surrogate of the task such that thetask may be allocated resources and completed as if it were a match tothe closest match reference task fingerprint. In some examples,allowances and/or adjustments to the allocated resources may be madebased on the closeness of match. In general, the task fingerprint may beperformed by any suitable resource of a datacenter as discussed herein.In some examples, the fingerprint comparison may be performed by adedicated computing resource. In some examples, the fingerprintcomparison may be performed by a task manager at the datacenter.

In general, a task behavior profile may include any information relatedto performing the task at the datacenter. In some examples, the taskbehavior profile may include a preferred set of resources for performingthe task. In some examples, a set of resources may include a processortype and/or capability, a typical task duration, a memory type and/oramount, a cache space, or a virtual machine type or capability, or thelike. In some examples, the task behavior profile may include one ormore conflict tasks such that a conflict task may indicate that if theconflict task and the task may be allocated to the same resources, asubstantial slowdown and/or inefficiency may occur. As will beappreciated, when sharing resources, in many instances a minimal ormoderate inefficiency may occur. In some examples, a conflict resourcemay exist if the inefficiency may be above a predetermined threshold. Insome examples, the predetermined threshold may be a 50% slowdown. Insome examples, the predetermined threshold may be a 100% slowdown. Insome examples, the predetermined threshold may be a 200% slowdown. Insome implementations, the performance reduction may include a taskduration of five times a standard task duration. In some examples,allocating resources to perform the received task may include allocatingresources different than those being used by one or more of the conflicttasks. The process of method 100 may continue at block 150.

At block 150, “Allocate Resources to Perform the Received Task Based onthe Reference Task Behavior Profile”, resources, at the datacenter, maybe allocated to perform the received task based at least in part on thereference task behavior profile. In general, the resources may beallocated using any suitable technique or techniques. In some examples,the resources may be allocated by a task manager at the datacenter. Asdiscussed, in some examples, the task behavior profile may include oneor more conflict tasks. In some examples, allocating resources toperform the received task may include allocating resources differentthan those being used by one or more of the conflict tasks. In suchexamples, the resources may be allocated based on colocation dataobtained over time at the datacenter as discussed herein. As will beappreciated, avoiding such colocation performance inefficiencies maycause substantial performance improvements at the datacenter.

In some examples, the resources may be allocated based on theidentification of resource types, quantities, or combinations, or thelike that may most efficiently perform the task. In general, theallocated resources may include any suitable resources. In someexamples, the allocated resources may include a processor, a memory, acache space, or a virtual machine. As discussed, in some examples, thetask fingerprint may not be a perfect match to the reference taskfingerprint and a closest match reference task fingerprint or tasksurrogate may be used. The discussed techniques may provide effectiveapproximation matching for such task fingerprints such that the receivedtask may be substantially optimized based on the use of resourcesoptimized for the closest match reference task fingerprint or tasksurrogate. The process of method 100 may continue at block 160.

At block 160, “Perform the Received Task Using the Allocated Resources”,the received task may be performed using the allocated resources. Ingeneral, the received task may be performed using any suitable techniqueor techniques and may implement the allocated resources as discussed atblock 150. In some examples, the allocated resources may be availablecontinuously for the completion of the received task. In some examples,the allocated resources may be unavailable, become unavailable, or fail,or the like during the performing of the received task. In suchexamples, the datacenter may allocate other resources to perform thereceived task. The process of method 100 may continue at block 170.

At block 170, “Gather Performance Metrics Based on the Performance ofthe Task”, performance metrics based on the performance may be gathered.In general, the performance metrics may be gathered using any suitabletechnique or techniques. In general, the gathering of performancemetrics may be performed by any suitable resource of a datacenter asdiscussed herein. In some examples, the gathering of performance metricsmay be performed by a dedicated computing resource. In some examples,the gathering of performance metrics may be performed by a task profilerat the datacenter. In some examples, the performance metrics may becollected via hypervisors. In some examples, the performance metrics maybe collected via modified virtual machines such as a virtual platformarchitecture, or the like. Such modified virtual machines may providevisibility to operating system and/or virtual machine statuses includingcache usage or memory bandwidth, or the like. In some examples, theperformance metrics may be gathered using packet based network usagemonitoring at a datacenter.

In general, the performance metrics may include any suitable performancemetrics. In some examples, the performance metrics may includedatacenter management-relevant metrics. In some examples, theperformance metrics may include a task duration (i.e., how long a tasktakes to complete), a central processing unit (CPU) usage, a memoryusage, a network usage, a storage volume usage, a storage volume accessfrequency, a state of change of memory use, or a network traffic changeover time, or the like. In some examples, the performance metrics mayinclude predictive metrics such that the metrics may include statisticalprobabilities of various occurrences. Such metrics may be generated byanalyzing a task that may be repeated numerous times at the datacenterand may include a probability of an occurrence. As an illustrativeexample, a particular task (and related task fingerprint) may have a 90%probability of generating a 2 GB data storage access. Further, apredictive metric may be prior activity-dependent such that the metricmay have inputs. Using the discussed example, the 90% probability ofgenerating 2 GB data storage access may occur after a 1 MB (or more)network delivery from another task or application. Such metrics mayprovide the advantages of being substantially flexible, accurate, andruntime specific. The process of method 100 may continue at block 180.

At block 180, “Update the Reference Task Behavior Profile based on thePerformance Metrics”, the reference task behavior profile may be updatedbased at least in part on the gathered performance metrics. In general,the reference task behavior profile may be updated using any suitabletechnique or techniques. In general, the task behavior profile may beupdated by any suitable resource of a datacenter as discussed herein. Insome examples, the task behavior profile may be updated by a dedicatedcomputing resource. In some examples, task behavior profile may beupdated at a task profiler at the datacenter. As is discussed furtherherein, the task behavior profile implemented as a database and the taskbehavior profile may be updated by updating information in the database.In general, an update to the task behavior profile may include any ofthe behavior profile information as discussed herein. As discussed, insome examples, a perfect or substantially perfect match may have beendetermined between the task fingerprint and the closest match referencetask fingerprint. In such examples, an update to the task behaviorprofile may include updating the task behavior profile of the closestmatch reference task fingerprint. In some examples, the task fingerprintmay be a newly identified task fingerprint and updating the taskbehavior profile may include providing a new fingerprint and a newbehavior profile entry.

Method 100 and other techniques discussed herein may provide dynamicscheduling, dynamic capacity planning, substantial efficiencies,improved performance from the same hardware, or optimization andresource savings for a datacenter, particularly in the use of resourcesshared between two or more tasks. Method 100 may be performed any numberof times as tasks may be received at a datacenter. In some examples,method 100 may be performed for all received tasks. In some examples,method 100 may be performed for a portion of the received tasks or incertain situations, such as during certain load levels within adatacenter. As discussed, a task may be received any one of a pluralityof clients. In some examples, tasks received from different clients maybe fingerprinted, compared and used to allocate resources as discussedherein. Such implementations may offer the advantage of providingsubstantially more information for characterizing tasks and allocatingresources. Further, such implementations may offer the advantage ofefficiently allocating resources for smaller clients, clients that useservices less frequently, or new clients, or the like.

As discussed, techniques discussed herein may provide substantialefficiencies and resource savings for a datacenter. In some examples, adatabase may be generated that may be transferred to another datacenter,implemented at a new datacenter, or provided as a datacenter solution,or the like such that the information generated and retained may beleveraged for use at other datacenters.

FIG. 2 is an illustration of a flow diagram of an example method 200 forgenerating a task fingerprint and resource allocation database, arrangedin accordance with at least some embodiments of the present disclosure.In general, method 200 may be performed by any suitable device, devices,or system such as those discussed herein. In some examples, method 200may be performed by a datacenter or datacenter cluster, or the like. Insome examples, method 200 may be performed by a cloud computing service.In some examples, method 200 may be performed by system as discussedherein with respect to FIG. 7. In some examples, method 200 provides atask fingerprint and resource allocation database that may betransferred and/or transmitted to another datacenter or datacentercluster, or the like.

Method 200 sets forth various functional blocks or actions that may bedescribed as processing steps, functional operations, events and/oracts, etc., which may be performed by hardware, software, and/orfirmware. Numerous alternatives to the functional blocks shown in FIG. 2may be practiced in various implementations. For example, interveningactions not shown in FIG. 2 and/or additional actions not shown in FIG.2 may be employed and/or some of the actions shown in FIG. 2 may beeliminated, without departing from the scope of claimed subject matter.Method 200 may include one or more of functional operations as indicatedby one or more of blocks 210, 220, 230, 240, 250, 260, 270 and/or 280.The process of method 200 may begin at block 210.

At block 210, “Receive a Task”, a task may be received at a datacenter,for example. In general, the task may be received using any suitablecommunications technique or techniques. In general, the received taskmay include any suitable task, group of tasks, or subtask, application,process or the like as discussed herein and, in particular, with respectto block 110 of method 100. The process of method 200 may continue atblock 220.

At block 220, “Generate a Task Fingerprint Based on the Received Task”,a task fingerprint may be generated, at the datacenter, based at leastin part on the received task. In general, the task fingerprint may begenerated using any suitable technique or techniques that may enablerecognition and/or matching of recurring tasks received at thedatacenter. Any of the methods or techniques discussed herein may beused to generate the task fingerprint and in particular, those discussedwith respect to block 120 of method 100.

As discussed, in some examples, a task fingerprint may be generated at adatacenter. In some examples, a task fingerprint may be received from aclient such as, for example, a client system, or user system, or thelike. In such examples, the processing required to generate a taskfingerprint may thereby be offloaded from the data center to the clientside. In general, the task fingerprint may be formed at the client sideusing any of the techniques discussed herein and the task fingerprintmay be transmitted from the client side using any suitablecommunications techniques. The process of method 200 may continue atblock 230.

At block 230, “Allocate a Resource Set to Perform the Received Task”,resources, at the datacenter, may be allocated to perform the receivedtask. In general, the resources may be allocated using any suitabletechnique or techniques. In some examples, the resources may beallocated by a task manager at the datacenter. In some examples, theresources may be allocated to gather data on the task behavior duringperformance with the allocated resources. In some examples, theresources may be chosen randomly. In some examples, the resources may bechosen based on a resource template such that received tasks may all (atleast initially) be performed with a baseline resource set. In someexamples, the resources may be chosen based on characteristics of thetask fingerprint. In some examples, the resources may be chosen based ona comparison to reference task fingerprints and a reference taskbehavior profile as discussed herein and, in particular, with respect tomethod 100. In some examples, it may be desirable to determine conflicttasks for the task. In some examples, allocating resources may includeallocating resources already performing another task to determinewhether a task conflict may exist. In some examples, it may be desirableto determine a minimal resource amount that may perform a task. Ingeneral, the allocated resources may include any suitable resources. Insome examples, the allocated resources may include a processor, amemory, a cache space, or a virtual machine, or the like. The process ofmethod 200 may continue at block 240.

At block 240, “Perform the Received Task Using the Allocated ResourceSet”, the received task may be performed using the allocated resources.In general, the received task may be performed using any suitabletechnique or techniques and may implement the allocated resources asdiscussed at block 230. The received task may be performed using any ofthe techniques discussed herein. The process of method 200 may continueat block 250.

At block 250, “Gather a Task Behavior for the Performed Task”,performance metrics based on the performance may be gathered. Ingeneral, the performance metrics may be gathered using any suitabletechnique or techniques. In general, the gathering of performancemetrics may be performed by any suitable resource of a datacenter asdiscussed herein. In some examples, the gathering of performance metricsmay be performed by a dedicated computing resource such as a taskprofiler. In some examples, the performance metrics may be collected viahypervisors, via modified virtual machines such as a virtual platformarchitecture, or using packet based network usage monitoring, or thelike.

In general, the performance metrics may include any suitable performancemetrics. In some examples, the performance metrics may includedatacenter management-relevant metrics. In some examples, theperformance metrics may include a task duration, a central processingunit (CPU) usage, a memory usage, a network usage, a storage volumeusage, a storage volume access frequency, a state of change of memoryuse, or a network traffic change over time, or the like. In someexamples, the performance metrics may include predictive metrics suchthat the metrics may include statistical probabilities as discussedherein. The process of method 200 may continue at block 260.

At block 260, “Is the Task Fingerprint in the Database?”, it may bedetermined whether the task fingerprint may be in the task fingerprintand resource allocation database. In general, such a determination maybe made using any suitable technique or techniques. In some examples, itmay be determined whether the task fingerprint and a task fingerprint inthe database substantially match. In some examples, the task fingerprintmay be compared to reference task fingerprints that may already populatethe task fingerprint and resource allocation database. In some examples,the comparison may include comparing the results of hash functionsrelated to the task fingerprint and the reference task fingerprints,comparing the results of sub-fingerprint masking related to the taskfingerprint and the reference task fingerprints, or comparing theresults of tree flow extractions and symbolic representations related tothe task fingerprint and the reference task fingerprints, or the like.In general, such comparison may be performed by any suitable resource ofa datacenter as discussed herein such as, for example, a task manager.If the task fingerprint may not be in the task fingerprint and resourceallocation database, the process of method 200 may continue at block270. If the task fingerprint may be in the task fingerprint and resourceallocation database, the process of method 200 may continue at block280.

At block 270, “Relate the Task Fingerprint, the Allocated Resource Set,and the Task Behavior in the Database as a New Entry”, the taskfingerprint, the allocated resource set, and the task behavior may berelated in the database as a new entry. In general, the described datamay be entered and/or maintained in the database using any suitabletechniques and/or data structures. In some examples, the taskfingerprint, the allocated resource set, and the task behavior may berelated as a table. In some examples, the task fingerprint, theallocated resource set, and the task behavior may be related in thedatabase in a table as discussed with respect to FIGS. 4 and/or 5herein. In some examples, the task fingerprint, the allocated resourceset, and the task behavior may be related in a relational database. Aswill be appreciated, a variety of information may be related to thediscussed task performance in the database in addition to the discusseddata such as, for example, a time stamp, data related to performanceparameters of the datacenter, or data related to other tasks beingconcurrently performed by the datacenter, or the like.

At block 280, “Relate the Allocated Resource Set and the Task Behaviorto the Task Fingerprint Entry in the Database”, the allocated resourceset and the task behavior may be related to the task fingerprint in thedatabase as a common task fingerprint entry. In general, the describeddata may be entered and/or maintained in the database using any suitabletechniques and/or data structures. In some examples, the allocatedresource set and the task behavior may be related to the taskfingerprint as a table or as a table entry, or the like. In someexamples, the allocated resource set and the task behavior may berelated to the task fingerprint as a table entry as discussed withrespect to FIGS. 4 and/or 5 herein. In some examples, the allocatedresource set and the task behavior may be related to the taskfingerprint in a relational database. As discussed, a variety ofadditional information may be related to the discussed task performancein the database such as, for example, a time stamp, data related toperformance parameters of the datacenter, or data related to other tasksbeing concurrently performed by the datacenter, or the like.

Method 200 and other techniques discussed herein may be performed toprovide a task fingerprint and resource allocation database. Method 200may be performed any number of times as tasks may be received at adatacenter. As will be appreciated, the task fingerprint and resourceallocation database may become more complete, robust, and/or built-outas more tasks may be received, performed, and characterized, asdiscussed herein. In some examples, the task fingerprint and resourceallocation database may be periodically restructured, culled ofquestionable information, combined or compared with other databases, orthe like, to provide proper database management.

As discussed in method 200, a task fingerprint and resource allocationdatabase may be built and updated over time. Such a database may beuseful to an implementing datacenter or the database may be used byanother datacenter, start up datacenter, or the like. As will beappreciated, method 200 may be discussed in the context of a functionaldatacenter performing useful client tasks and a resulting taskfingerprint and resource allocation database. In some examples, a methodusing techniques similar to those discussed with respect to method 200may be used on a series of test tasks to build the task fingerprint andresource allocation database. In such examples, the tasks may be chosenfrom known common tasks, generated for testing purposes, or the like.Such techniques may offer the advantages of allowing a databasestructure to be fully and/or predictably populated by the attained testresults.

As discussed herein, in some examples, a task fingerprint and resourceallocation database may be generated. In some examples, such a databasemay be utilized for allocating resources. FIG. 3 is an illustration of atable 300 of an example data structure for related task fingerprint,allocated resource set, and task behavior information, arranged inaccordance with at least some embodiments of the present disclosure. Asshown in FIG. 3, table 300 may include a task fingerprint column 310, aresource set row 320, and corresponding task behavior entries 330 and/orcorresponding empty entries 340. In general, table 300 may be arepresentation of a data set implemented in a database that may bemaintained at a datacenter or system as discussed herein.

In general, table 300 may include any number of task fingerprint entries(1, 2, . . . X) in task fingerprint column 310, any number resource setentries (1, 2, . . . Y) in resource set row 320, any number of taskbehavior entries 330, and any number of empty entries 340. In someexamples, task fingerprint entries in task fingerprint column 310 mayinclude task fingerprints of tasks performed by a datacenter, asdiscussed herein. In general, the task fingerprint entries may includeany of the fingerprints discussed herein. In some examples, resource setentries may include allocated resource sets used to perform tasks, asdiscussed herein.

In general, task behavior entries 330 may include any task behaviorinformation as discussed herein. In general, the task behavior entriesmay include any information related to performing the task related tothe task fingerprint using the resource set at the datacenter. In someexamples, the task behavior entries may include performance metrics, asdiscussed herein. In some examples, the task behavior entries mayinclude datacenter management-relevant metrics. In some examples, thetask behavior entries may include a task duration, a central processingunit (CPU) usage, a memory usage, a network usage, a storage volumeusage, a storage volume access frequency, a state of change of memoryuse, or a network traffic change over time, or the like. In someexamples, the task behavior entries may include predictive metrics suchthat the metrics may include probabilities. In some examples, the taskbehavior entries may include one or more conflict tasks, as discussedherein. In some examples, table 300 may include a pointer or a flag thatmay, for each task fingerprint, identify a resource set andcorresponding task behavior entries that may indicate a preferredresource set. Such a resource set and/or task behavior entries mayindicate a reference task behavior profile for use in allocatingresources for a closest match reference task fingerprint, as discussedherein. In general, empty entries 340 may include no information and mayinclude entries for task fingerprint and resource set combinations thathave evaluated at the datacenter.

FIG. 4 is an illustration of a table of an example data structure forrelated task fingerprints and task behavior information, arranged inaccordance with at least some embodiments of the present disclosure. Asshown in FIG. 4, table 400 may include a task fingerprint column 410, atask fingerprint row 420, and corresponding task behavior entries 430and/or corresponding empty entries 440. As shown, table 400 may alsoinclude diagonal task behavior entries 450 that may include taskbehavior entries that may contain information for instances when thesame task has been collocated on a shared resource. In some examples,table 400 may be implemented as an upper or lower triangular matrix ortable such that any repeated information may be removed. In someexamples, table 400 may be implemented as a full table and the uppertriangle may include high priority processes and/or tasks and the lowertriangle may include lower priority processes and/or tasks. In general,table 400 may be a representation of a data set implemented in adatabase that may be maintained at a datacenter or system as discussedherein.

In general, table 400 may include any number of task fingerprint entries(1, 2, . . . X) in task fingerprint column 410 and task fingerprint row420, any number of task behavior entries 430, and any number emptyentries 440. In some examples, task fingerprint entries in taskfingerprint column 410 and task fingerprint row 420 may include taskfingerprints of tasks performed by a datacenter, as discussed herein. Ingeneral, the task fingerprint entries may include any of thefingerprints discussed herein. In some examples, table 400 may bearranged as shown such that task behavior entries 430 may includeinformation related to sharing resources between tasks (as representedby task fingerprints). As such, conflict resources as discussed hereinwhich may cause inefficiencies at the datacenter may be easilyidentified.

In general, task behavior entries 430 may include any task behaviorinformation as discussed herein. In general, the task behavior entriesmay include any information related to using shared resources tosubstantially simultaneously perform the tasks related to the taskfingerprints using resources at the datacenter. In some examples, thetask behavior entries may include performance metrics, as discussedherein. In some examples, the task behavior entries may includedatacenter management-relevant metrics. In some examples, the taskbehavior entries may include a task duration, a central processing unit(CPU) usage, a memory usage, a network usage, a storage volume usage, astorage volume access frequency, a state of change of memory use, or anetwork traffic change over time, or the like. In some examples, thetask behavior entries may include predictive metrics such that themetrics may include probabilities. In some examples, table 400 mayinclude a pointer or a flag that may, identify corresponding tasks thatmay cause the discussed inefficiencies, and thereby indicate conflicttasks. In general, empty entries 440 may include no information and mayinclude entries for task fingerprint and resource set combinations thathave evaluated at the datacenter.

As discussed herein, in some examples, a task fingerprint may begenerated by performing a tree flow extraction on the received task andproviding a symbolic approximation representation of the tree flowextraction. In some examples, tree flow extraction may provide arepresentation of the behavioral structure of a task and may includecomponents for data content of the task. FIG. 5 is an illustration of adiagram 500 of an example tree flow extraction, arranged in accordancewith at least some embodiments of the present disclosure. As shown inFIG. 5, diagram 500 may include code segments 510 (which are numbered1-63 in diagram 500) that may be linked by connections 520. In someexamples, diagram 500 may also include segments 530. In general, codesegments 510 may represent execution units in the tree flow extractedtask. In general, connections 520 may represent execution flows or linksbetween execution units. In general, segments 530 may indicate thefrequency and/or probability that code segments 510 may be called duringthe execution of the task. As discussed herein, a tree flow extractionsuch as the example tree flow extraction of diagram 500 may converted toa symbolic approximation format using any suitable technique ortechniques.

As discussed herein, in some examples, a task fingerprint may begenerated by performing a tree flow extraction on the received task andproviding a symbolic approximation representation of the tree flowextraction. In some examples, the tree flow extraction may provide arepresentation of the behavioral structure of a task and may includecomponents for data content of the task. In some examples, the tree flowextraction may provide a signature related to the task. In general, thetree flow extraction may be performed using any suitable technique ortechniques. In some examples, the tree flow extraction may be performedvia access a hypervisor may have to a virtual machine file system. Insome examples, the tree flow extraction may include performing anextraction on compiled bytecode, scripts, or raw code, or the like of atask. In some examples, the tree flow extraction may be performed duringa task package upload and/or installation. Such implementations mayprovide the advantage of not requiring alterations to datacenterhypervisors. In such implementations, the task fingerprint determined bytree flow extraction may be encrypted and stored alongside a virtualmachine at the datacenter.

In general, the system architectures, methods, and databases discussedherein may be implemented in any datacenter, system and/or computingsystem using any suitable computer readable media or computer programproducts. Example computer program products are described with respectto FIG. 6 and elsewhere herein. Example systems are described withrespect to FIG. 7 and elsewhere herein. In some examples, a datacenter,system, or data cluster, or other system as discussed herein may beimplemented over multiple physical sites or locations.

FIG. 6 illustrates an example computer program product 600, arranged inaccordance with at least some embodiments of the present disclosure.Computer program product 600 may include machine readable non-transitorymedium having stored therein a plurality of instructions that, whenexecuted, cause the machine to allocate datacenter resources accordingto the processes and methods discussed herein. Computer program product600 may include a signal bearing medium 602. Signal bearing medium 602may include one or more machine-readable instructions 604, which, whenexecuted by one or more processors, may operatively enable a computingdevice to provide the functionality described herein. In variousexamples, some or all of the machine-readable instructions may be usedby the devices discussed herein.

In some implementations, signal bearing medium 602 may encompass acomputer-readable medium 606, such as, but not limited to, a hard diskdrive, a Compact Disc (CD), a Digital Versatile Disk (DVD), a digitaltape, memory, etc. In some implementations, signal bearing medium 602may encompass a recordable medium 608, such as, but not limited to,memory, read/write (R/W) CDs, R/W DVDs, etc. In some implementations,signal bearing medium 602 may encompass a communications medium 610,such as, but not limited to, a digital and/or an analog communicationmedium (e.g., a fiber optic cable, a waveguide, a wired communicationlink, a wireless communication link, etc.). In some examples, signalbearing medium 602 may encompass a machine readable non-transitorymedium.

FIG. 7 is a block diagram illustrating an example computing device 700,arranged in accordance with at least some embodiments of the presentdisclosure. In various examples, computing device 700 may be configuredto allocate datacenter resources as discussed herein. In variousexamples, computing device 700 may be configured to allocate datacenterresources as a server system as discussed herein. In one example basicconfiguration 701, computing device 700 may include one or moreprocessors 710 and system memory 720. A memory bus 730 can be used forcommunicating between the processor 710 and the system memory 720.

Depending on the desired configuration, processor 710 may be of any typeincluding but not limited to a microprocessor (μP), a microcontroller(μC), a digital signal processor (DSP), or any combination thereof.Processor 710 can include one or more levels of caching, such as a levelone cache 711 and a level two cache 712, a processor core 713, andregisters 714. The processor core 713 can include an arithmetic logicunit (ALU), a floating point unit (FPU), a digital signal processingcore (DSP Core), or any combination thereof. A memory controller 715 canalso be used with the processor 710, or in some implementations thememory controller 715 can be an internal part of the processor 710.

Depending on the desired configuration, the system memory 720 may be ofany type including but not limited to volatile memory (such as RAM),non-volatile memory (such as ROM, flash memory, etc.) or any combinationthereof. System memory 720 may include an operating system 721, one ormore applications 722, and program data 724. Application 722 may includetask fingerprint/allocation application 723 that can be arranged toperform the functions, actions, and/or operations as described hereinincluding the functional blocks, actions, and/or operations describedherein. Program Data 724 may include task fingerprint/allocation data725 for use with task fingerprint/allocation application 723. In someexample embodiments, application 722 may be arranged to operate withprogram data 724 on an operating system 721. This described basicconfiguration is illustrated in FIG. 7 by those components within dashedline 701.

Computing device 700 may have additional features or functionality, andadditional interfaces to facilitate communications between the basicconfiguration 701 and any required devices and interfaces. For example,a bus/interface controller 740 may be used to facilitate communicationsbetween the basic configuration 701 and one or more data storage devices750 via a storage interface bus 741. The data storage devices 750 may beremovable storage devices 751, non-removable storage devices 752, or acombination thereof. Examples of removable storage and non-removablestorage devices include magnetic disk devices such as flexible diskdrives and hard-disk drives (HDD), optical disk drives such as compactdisk (CD) drives or digital versatile disk (DVD) drives, solid statedrives (SSD), and tape drives to name a few. Example computer storagemedia may include volatile and nonvolatile, removable and non-removablemedia implemented in any method or technology for storage ofinformation, such as computer readable instructions, data structures,program modules, or other data.

System memory 720, removable storage 751 and non-removable storage 752are all examples of computer storage media. Computer storage mediaincludes, but is not limited to, RAM, ROM, EEPROM, flash memory or othermemory technology, CD-ROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which maybe used to store the desired information and which may be accessed bycomputing device 700. Any such computer storage media may be part ofdevice 700.

Computing device 700 may also include an interface bus 742 forfacilitating communication from various interface devices (e.g., outputinterfaces, peripheral interfaces, and communication interfaces) to thebasic configuration 701 via the bus/interface controller 740. Exampleoutput interfaces 760 may include a graphics processing unit 761 and anaudio processing unit 762, which may be configured to communicate tovarious external devices such as a display or speakers via one or moreNV ports 763. Example peripheral interfaces 770 may include a serialinterface controller 771 or a parallel interface controller 772, whichmay be configured to communicate with external devices such as inputdevices (e.g., keyboard, mouse, pen, voice input device, touch inputdevice, etc.) or other peripheral devices (e.g., printer, scanner, etc.)via one or more I/O ports 773. An example communication interface 780includes a network controller 781, which may be arranged to facilitatecommunications with one or more other computing devices 783 over anetwork communication via one or more communication ports 782. Acommunication connection is one example of a communication media.Communication media may typically be embodied by computer readableinstructions, data structures, program modules, or other data in amodulated data signal, such as a carrier wave or other transportmechanism, and may include any information delivery media. A “modulateddata signal” may be a signal that has one or more of its characteristicsset or changed in such a manner as to encode information in the signal.By way of example, and not limitation, communication media may includewired media such as a wired network or direct-wired connection, andwireless media such as acoustic, radio frequency (RF), infrared (IR) andother wireless media. The term computer readable media as used hereinmay include both storage media and communication media.

Computing device 700 may be implemented as a portion of a small-formfactor portable (or mobile) electronic device such as a cell phone, amobile phone, a tablet device, a laptop computer, a personal dataassistant (PDA), a personal media player device, a wireless web-watchdevice, a personal headset device, an application specific device, or ahybrid device that includes any of the above functions. Computing device700 may also be implemented as a personal computer including both laptopcomputer and non-laptop computer configurations. In addition, computingdevice 700 may be implemented as part of a wireless base station orother wireless system or device.

Some portions of the foregoing detailed description are presented interms of algorithms or symbolic representations of operations on databits or binary digital signals stored within a computing system memory,such as a computer memory. These algorithmic descriptions orrepresentations are examples of techniques used by those of ordinaryskill in the data processing arts to convey the substance of their workto others skilled in the art. An algorithm is here, and generally, isconsidered to be a self-consistent sequence of operations or similarprocessing leading to a desired result. In this context, operations orprocessing involve physical manipulation of physical quantities.Typically, although not necessarily, such quantities may take the formof electrical or magnetic signals capable of being stored, transferred,combined, compared or otherwise manipulated. It has proven convenient attimes, principally for reasons of common usage, to refer to such signalsas bits, data, values, elements, symbols, characters, terms, numbers,numerals or the like. It should be understood, however, that all ofthese and similar terms are to be associated with appropriate physicalquantities and are merely convenient labels. Unless specifically statedotherwise, as apparent from the following discussion, it is appreciatedthat throughout this specification discussions utilizing terms such as“processing,” “computing,” “calculating,” “determining” or the likerefer to actions or processes of a computing device, that manipulates ortransforms data represented as physical electronic or magneticquantities within memories, registers, or other information storagedevices, transmission devices, or display devices of the computingdevice.

The foregoing detailed description has set forth various embodiments ofthe devices and/or processes via the use of block diagrams, flowcharts,and/or examples. Insofar as such block diagrams, flowcharts, and/orexamples contain one or more functions and/or operations, it will beunderstood by those within the art that each function and/or operationwithin such block diagrams, flowcharts, or examples can be implemented,individually and/or collectively, by a wide range of hardware, software,firmware, or virtually any combination thereof. In some embodiments,several portions of the subject matter described herein may beimplemented via Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Arrays (FPGAs), digital signal processors (DSPs), orother integrated formats. However, those skilled in the art willrecognize that some aspects of the embodiments disclosed herein, inwhole or in part, can be equivalently implemented in integratedcircuits, as one or more computer programs running on one or morecomputers (e.g., as one or more programs running on one or more computersystems), as one or more programs running on one or more processors(e.g., as one or more programs running on one or more microprocessors),as firmware, or as virtually any combination thereof, and that designingthe circuitry and/or writing the code for the software and or firmwarewould be well within the skill of one of skill in the art in light ofthis disclosure. In addition, those skilled in the art will appreciatethat the mechanisms of the subject matter described herein are capableof being distributed as a program product in a variety of forms, andthat an illustrative embodiment of the subject matter described hereinapplies regardless of the particular type of signal bearing medium usedto actually carry out the distribution. Examples of a signal bearingmedium include, but are not limited to, the following: a recordable typemedium such as a flexible disk, a hard disk drive (HDD), a Compact Disc(CD), a Digital Versatile Disk (DVD), a digital tape, a computer memory,etc.; and a transmission type medium such as a digital and/or an analogcommunication medium (e.g., a fiber optic cable, a waveguide, a wiredcommunication link, a wireless communication link, etc.).

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely examples and that in fact many other architectures can beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected”, or“operably coupled”, to each other to achieve the desired functionality,and any two components capable of being so associated can also be viewedas being “operably couplable”, to each other to achieve the desiredfunctionality. Specific examples of operably couplable include but arenot limited to physically mateable and/or physically interactingcomponents and/or wirelessly interactable and/or wirelessly interactingcomponents and/or logically interacting and/or logically interactablecomponents.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity.

It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to inventions containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). In those instances where aconvention analogous to “at least one of A, B, or C, etc.” is used, ingeneral such a construction is intended in the sense one having skill inthe art would understand the convention (e.g., “a system having at leastone of A, B, or C” would include but not be limited to systems that haveA alone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

While certain example techniques have been described and shown hereinusing various methods and systems, it should be understood by thoseskilled in the art that various other modifications may be made, andequivalents may be substituted, without departing from claimed subjectmatter. Additionally, many modifications may be made to adapt aparticular situation to the teachings of claimed subject matter withoutdeparting from the central concept described herein. Therefore, it isintended that claimed subject matter not be limited to the particularexamples disclosed, but that such claimed subject matter also mayinclude all implementations falling within the scope of the appendedclaims, and equivalents thereof.

What is claimed:
 1. A method for allocating resources at a datacentercomprising: receiving a task at the datacenter; generating, at thedatacenter, a task fingerprint based at least in part on the receivedtask; comparing, at the datacenter, the task fingerprint to a pluralityof reference task fingerprints to determine a closest match referencetask fingerprint from the plurality of reference task fingerprints;determining, at the datacenter, a reference task behavior profilerelated to the closest match reference task fingerprint; and allocating,at the datacenter, one or more resources to perform the received taskbased at least in part on the reference task behavior profile.
 2. Themethod of claim 1, further comprising: performing, at the datacenter,the task using the allocated resources; gathering one or moreperformance metrics based at least in part on the performance of thetask; and updating, at the datacenter, the reference task behaviorprofile related to the closest match reference task fingerprint based atleast in part on the one or more performance metrics.
 3. The method ofclaim 2, wherein the one or more performance metrics comprise at leastone of a task duration, a central processing unit (CPU) usage, a memoryusage, a network usage, a storage volume usage, a storage volume accessfrequency, a state of change of memory use, or a network traffic changeover time.
 4. The method of claim 2, further comprising: receiving asecond task at the datacenter; generating, at the datacenter, a secondtask fingerprint based at least in part on the received second task;comparing, at the datacenter, the second task fingerprint to theplurality of reference task fingerprints; determining the closest matchreference task fingerprint is the closest match to the second taskfingerprint; and allocating, at the datacenter, second resources toperform the received second task based at least in part on the updatedreference task behavior profile.
 5. The method of claim 4, furthercomprising: performing, at the datacenter, the second task using thesecond allocated resources; gathering one or more second performancemetrics based at least in part on the performance of the task; andupdating, at the datacenter, the reference task behavior profile relatedto the closest match reference task fingerprint based on the one or moresecond performance metrics.
 6. The method of claim 5, wherein updatingthe reference task behavior profile comprises providing a probabilitybased behavior metric including a percentage chance an allocatedresource will reach a threshold level.
 7. The method of claim 1, whereinthe reference task behavior profile comprises one or more conflict tasksand allocating resources to perform the received task includesallocating resources different than those being used by one or more ofthe conflict tasks.
 8. The method of claim 1, wherein generating thetask fingerprint comprises performing a hash function on the receivedtask.
 9. The method of claim 1, wherein generating the task fingerprintcomprises performing a tree flow extraction on the received task andproviding a symbolic approximation representation of the tree flowextraction.
 10. The method of claim 1, wherein generating the taskfingerprint comprises sub-fingerprint masking some of a plurality ofsubtasks within the task and identifying unmasked subtasks of the task.11. The method of claim 1, wherein generating the task fingerprintcomprises accessing the received task using at least one of a hypervisorlevel access, a board level access, a processor core level access, or avirtual machine level access.
 12. The method of claim 1, wherein the oneor more allocated resources comprise at least one of a processor, amemory, a cache space, or a virtual machine.
 13. The method of claim 1,wherein the task fingerprint and the closest match reference fingerprintare a perfect match.
 14. The method of claim 1, wherein the taskcomprises at least one of an application or a process.
 15. A method forgenerating a task fingerprint and resource allocation databasecomprising: receiving a plurality of tasks at a datacenter; generating,at the datacenter, a task fingerprint for each received task to form aplurality of task fingerprints; allocating, at the datacenter, aresource set to each received task to define a plurality of allocatedresource sets; performing, at the datacenter, each received task usingthe respective allocated resource set; gathering, at the datacenter, atask behavior for each performed task using the allocated resource setto generate a plurality of task behaviors; relating, in a database atthe datacenter, each task fingerprint, allocated resource set, and taskbehavior to generate the task fingerprint and resource allocationdatabase.
 16. The method of claim 15, further comprising: comparing atleast a first and a second task fingerprint of the plurality of taskfingerprints; determining whether the first and the second taskfingerprints substantially match; and if the first and the second taskfingerprints substantially match: relating database entries for thefirst and the second task fingerprints as a common task fingerprintentry.
 17. The method of claim 16, wherein the first and the second taskfingerprints match if, upon performing a matching algorithm, the resultsfor the first and the second fingerprints are within a predeterminedthreshold.
 18. The method of claim 15, wherein each task behaviorcomprises a performance metric, and wherein the performance metricincludes at least one of a task duration, a central processing unit(CPU) usage, a memory usage, a network usage, a storage volume usage, astorage volume access frequency, a state of change of memory use, or anetwork traffic change over time.
 19. A machine readable non-transitorymedium having stored therein instructions that, when executed, cause adatacenter to allocate resources by: receiving a task; generating a taskfingerprint based at least in part on the received task; comparing thetask fingerprint to a plurality of reference task fingerprints todetermine a closest match reference task fingerprint from the pluralityof reference task fingerprints; determining a reference task behaviorprofile related to the closest match reference task fingerprint; andallocating one or more resources to perform the received task based atleast in part on the reference task behavior profile.
 20. The machinereadable non-transitory medium of claim 19 having stored therein furtherinstructions that, when executed, cause the datacenter to allocateresources by: performing the task using the allocated resources;gathering one or more performance metrics based at least in part on theperformance of the task; and updating the reference task behaviorprofile related to the closest match reference task fingerprint based atleast in part on the one or more performance metrics.
 21. The machinereadable non-transitory medium of claim 20 having stored therein furtherinstructions that, when executed, cause the datacenter to allocateresources by: receiving a second task; generating a second taskfingerprint based at least in part on the received second task;comparing the second task fingerprint to the plurality of reference taskfingerprints; determining the closest match reference task fingerprintis the closest match to the second task fingerprint; and allocatingsecond resources to perform the received second task based at least inpart on the updated reference task behavior profile.
 22. A machinereadable non-transitory medium having stored therein instructions that,when executed, cause a datacenter to generate a task fingerprint andresource allocation database by: receiving a plurality of tasks;generating a task fingerprint for each received task to form a pluralityof task fingerprints; allocating a resource set to each received task todefine a plurality of allocated resource sets; performing each receivedtask using the respective allocated resource set; gathering a taskbehavior for each performed task using the allocated resource set togenerate a plurality of task behaviors; and relating each taskfingerprint, allocated resource set, and task behavior to generate thetask fingerprint and resource allocation database.
 23. The machinereadable non-transitory medium of claim 22 having stored therein furtherinstructions that, when executed, cause the datacenter to generate atask fingerprint and resource allocation database by: comparing at leasta first and a second task fingerprint of the plurality of taskfingerprints; determining whether the first and the second taskfingerprints substantially match; and if the first and the second taskfingerprints substantially match: relating database entries for thefirst and the second task fingerprints as a common task fingerprintentry.
 24. A datacenter comprising: a machine readable medium havingstored therein instructions that, when executed, cause the datacenter toallocate resources by: receiving a task; generating a task fingerprintbased at least in part on the received task; comparing the taskfingerprint to a plurality of reference task fingerprints to determine aclosest match reference task fingerprint from the plurality of referencetask fingerprints; determining a reference task behavior profile relatedto the closest match reference task fingerprint; and allocating one ormore resources to perform the received task based at least in part onthe reference task behavior profile; and a processor coupled to themachine readable medium to execute the instructions.
 25. The datacenterof claim 24, wherein the machine readable medium has stored thereinfurther instructions that, when executed, cause the datacenter toallocate resources by: performing the task using the allocatedresources; gathering one or more performance metrics based at least inpart on the performance of the task; and updating the reference taskbehavior profile related to the closest match reference task fingerprintbased at least in part on the one or more performance metrics.
 26. Thedatacenter of claim 25, wherein the machine readable medium has storedtherein further instructions that, when executed, cause the datacenterto allocate resources by: receiving a second task; generating a secondtask fingerprint based at least in part on the received second task;comparing the second task fingerprint to the plurality of reference taskfingerprints; determining the closest match reference task fingerprintis the closest match to the second task fingerprint; and allocatingsecond resources to perform the received second task based at least inpart on the updated reference task behavior profile.
 27. A datacentercomprising: a machine readable medium having stored therein instructionsthat, when executed, cause the datacenter to generate a task fingerprintand resource allocation database by: receiving a plurality of tasks;generating a task fingerprint for each received task to form a pluralityof task fingerprints; allocating a resource set to each received task todefine a plurality of allocated resource sets; performing each receivedtask using the respective allocated resource set; gathering a taskbehavior for each performed task using the allocated resource set togenerate a plurality of task behaviors; and relating, in a database,each task fingerprint, allocated resource set, and task behavior togenerate the task fingerprint and resource allocation database; and aprocessor coupled to the machine readable medium to execute theinstructions.
 28. The datacenter of claim 27, wherein the machinereadable medium has stored therein further instructions that, whenexecuted, cause the datacenter to generate a task fingerprint andresource allocation database by: comparing at least a first and a secondtask fingerprint of the plurality of task fingerprints; determiningwhether the first and the second task fingerprints substantially match;and if the first and the second task fingerprints substantially match:relating database entries for the first and the second task fingerprintsas a common task fingerprint entry.